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OpenClaw Consensus

该 Skill 把同一份 brief 依次交给至少 2 个 API 后端模型进行固定 2 轮审议,保留每轮产物,再由当前会话模型写出 final.md 综合,呈现共识、分歧、未决不确定点与升级点。运行前需用户确认 brief 与显式模型清单;选定模型失败时要求直接报告,不得静默替换或临场变通。

pstepien-labs
pstepien-labs
热度 35.74
最近更新 2026-05-11 17:46:54
分类:自动化依赖:代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景:工作流 / 研究 / 摘要总结

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add openclaw-consensus
Metadata
name
openclaw-consensus
description
Run a fixed 2-round cross-model deliberation through the repo-local OpenClaw Consensus runtime.
metadata
{"openclaw":{"emoji":"🧭","requires":{"bins":["node","openclaw"]}}}

OpenClaw Consensus

Use this skill when the user explicitly wants a bounded cross-model deliberation on one brief.

What this skill does

  • runs the same brief through 2-4 explicitly selected API-backed models from the active OpenClaw-configured pool
  • preserves round-1 and round-2 artifacts on disk
  • asks the current session model to write the final synthesis

Do not use this skill when

  • the user wants casual chat or a single quick answer
  • the user did not agree to explicit model selection yet
  • the request is framed as replacing a lawyer, accountant, doctor, engineer, or other expert

Required inputs

Before running, confirm all of these:

  1. one final brief
  2. an explicit model shortlist of at least 2 models
  3. an optional run label if the user wants one

Model rules

  • In this MVP, the model shortlist is required.
  • Only use models that appear in node {baseDir}/src/cli.mjs models.
  • Do not silently substitute a missing or failing model.
  • Do not use ollama/* models in this repo's MVP.

Invocation flow

  1. If needed, inspect the configured model pool:
    node {baseDir}/src/cli.mjs models
    
  2. Run the consensus flow with the brief, explicit model list, and the current session model as orchestrator:
    node {baseDir}/src/cli.mjs run --brief "<brief>" --models "openai-codex/gpt-5.4,openai-codex/gpt-5.5" --orchestrator-model "<current-session-model>" --label "optional-label"
    
  3. Read back the generated final.md and run.json before summarizing for the user.

Output expectations

Read the final synthesis carefully and present it truthfully:

  • consensus
  • disagreements
  • unresolved uncertainty
  • escalation points
  • best overall synthesis

Safety rules

  • Preserve disagreement; do not flatten it in your summary.
  • Consensus is not proof of correctness.
  • Keep expert-escalation language narrow and honest.
  • If the CLI reports a fallback or unavailable selected model, stop and surface the failure clearly instead of improvising around it.